import sys
import os
import pytest
from sklearn.pipeline import Pipeline
from unittest.mock import patch, MagicMock

# Ensure src is in path
sys.path.append(os.getcwd())

from src.train import get_pipeline, train

def test_get_pipeline_structure():
    """Test if get_pipeline returns a valid Scikit-learn pipeline."""
    pipeline = get_pipeline("rf")
    assert isinstance(pipeline, Pipeline)
    assert "preprocessor" in pipeline.named_steps
    assert "classifier" in pipeline.named_steps

def test_train_function_runs(tmp_path):
    """
    Test if the train function runs without errors.
    We mock generate_models to use a temp dir and run with small data.
    """
    # Create a temporary directory for models
    models_dir = tmp_path / "models"
    model_path = models_dir / "model.pkl"
    
    # Needs to be string for some os.path usages if they are strict, but pathlib usually works.
    # However, src/train.py uses os.path.join(MODELS_DIR, ...), so we need to patch constants.
    
    with patch("src.train.MODELS_DIR", str(models_dir)), \
         patch("src.train.MODEL_PATH", str(model_path)), \
         patch("src.train.generate_data") as mock_gen:
        
        # Mock data generation to return a very small dataframe to speed up test
        # We need to use real data structure though bc pipeline expects specific columns
        from src.data import generate_data
        real_small_df = generate_data(n_samples=10)
        mock_gen.return_value = real_small_df
        
        # Run training
        try:
            train()
        except Exception as e:
            pytest.fail(f"Train function failed with error: {e}")
            
        # Check if model file was created
        assert model_path.exists()